Fingerprint Verification System

Good quality Image

Good quality Fingerprint Image

AuthenticationFingeprint Image Fingerprint

Image Enhancement

Minutiae Feature

Extraction

Matching methods

Database

Minutiae features

ImagePreprocessing

Fingerprint Segmentation

Separation of fingerprint area (foreground) from the image background

• Traditional methods use block level features– Local histogram of ridge orientation– Gray-level variance– Magnitude of the gradient in each image block– Gabor feature

• My new method- point feature

Fingerprint Feature-Minutiae

Traditional Feature Detection Algorithm- Binarization-Thinning

– binarization followed by thinning step, the width of the ridges reduced to one pixel

– Location of minutiae points in the skeleton image • number of neighbor black pixels at a point of

interest in a 3 X 3 window• crossing number ( ending: cn(p) =1, bifurcation:

cn(p)=3, normal:cn(p) =2)– Thinning limitation: Aberrations and irregularity of the

binary ridge boundaries have an adverse effect on the skeletons, leads to the detection of spurious minutiae

New Minutiae Detection Method

Pout

Pin

Minutiae Point

Middle Point of SA and EB

(b) (c)

(a)

Pin

Pout

Pin × Pout

(d)

SA: Start Point of Pin

EB: End Point Pout

Pin × Pout

Figure 8 Minutiae Detection (a) Detection of turning points, (b) & (c) Vector cross product for determining the turning type, (d) Determining minutiae direction

Start

B

CF

Bifurcation

Post processing (Elimination of False Minutiae in the Image Boundary )

Determination of Turn Points• The ridge contours of fingerprint images can be consistently

traced in a counter-clockwise fashion

• Two types of turn points: left and right

• S(Pin, Pout) = x1y2 –x2y1

– Pin : Vector leading into the candidate point

– Pout: Vector leading out of the point of interest

– S(Pin, Pout) >0 indicates left turn, S(Pin, Pout) <0 indicates

right turn

– Significant turn can be determined by x1y1 + x2y2 < T

– Angle between Pin and Pout

IMAGE QUALITY MODELING -Proposed Limited Ring FFT Spectral Measures

the spectrum in polar coordinates, S(r, θ)

For each direction θ, Sθ( r ) – the spectrum behavior along a radial direction from the origin•For each frequency r, Sr(θ) – the spectrum behavior along a circle centered on the origin

Enhancement in High-curvature region of Fingerprint Image (2)

• Calculate the Gradients Gx, Gy• Calculate variances (Gxx, Gyy) and cross-

covariance (Gxy) of Gx and Gy• Calculate coherence mapsqrt((Gxx-Gyy)^2+4*Gxy^2)/(Gxx + Gyy)• Find the minimum coherence value in ROI• Add 0.1+ minimum (Coh)• Get the high curvature regions with region

property like centroid or bounding box

Enhancement Results

Enhancement resultsCore

Delta

Enhancement results